code: def crop_clip(clip, min_h, min_w, h, w):
      if isinstance(clip[0], np.ndarray):
          cropped = [
              img[min_h : min_h + h, min_w : min_w + w, :]
              for img in clip
          ]
  
      elif isinstance(clip[0], PIL.Image.Image):
          cropped = [
              img.crop((min_w, min_h, min_w + w, min_h + h))
              for img in clip
          ]
      else:
          raise TypeError(
              "Expected numpy.ndarray or PIL.Image"
              + "but got list of {0}".format(type(clip[0]))
          )
      return cropped
  
file_path: gluon-cv/gluoncv/torch/data/transforms/videotransforms/functional.py
imports: import numbers
  import cv2
  import numpy
  import PIL
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: crop_clip(clip, min_h, min_w, h, w) and returns: cropped